Qwen publishes official GGUF weights for its 8-billion-parameter vision-language model, Qwen3-VL-8B-Instruct, allowing local inference of image, video, and text workloads through common tools like llama.cpp, Ollama, LM Studio, and vLLM without needing a custom Transformers environment. The model supports a 256K-token context window, 32-language OCR, 2D/3D grounding, GUI agent capabilities, and video timestamp localization, all under an Apache 2.0 license.
- The language model is separated from a multimodal projector (mmproj) file, letting users mix precisions such as a 4-bit language model with an F16 projector for better OCR accuracy
- The Q4_K_M language model plus a quantized projector can run on 16 GB unified-memory systems, though the full 256K context window is impractical on that hardware
- Benchmark scores include DocVQA 96.1, MMMU 69.6, and MMLU-Pro 68.6
- The language model is separated from a multimodal projector (mmproj) file, letting users mix precisions such as a 4-bit language model with an F16 projector for better OCR accuracy
- The Q4_K_M language model plus a quantized projector can run on 16 GB unified-memory systems, though the full 256K context window is impractical on that hardware
- Benchmark scores include DocVQA 96.1, MMMU 69.6, and MMLU-Pro 68.6
Asif Razzaq writes that Google Research has open-sourced Harness², a framework that improves agent performance by recursively refining the surrounding harness—prompts, tools, memory, and orchestration—while keeping model weights frozen. It operates without ground-truth labels, using a Propose, Probe, Compose cycle to test candidate harness edits against observed behavior.
- Weaker executors gained more; GLM-5 beat base Gemini 3.5 Flash on WorkBuddy-Bench after one refinement step
- Parallel recursion at k=5 lifted Gemini under OpenCode to 80.8 on WorkBuddy
- The framework extends the 7-file split from earlier Agentic Harness Engineering work by 1 component
- Requires Linux, Python 3.12+, uv, and Vertex AI for model-backed runs
- Weaker executors gained more; GLM-5 beat base Gemini 3.5 Flash on WorkBuddy-Bench after one refinement step
- Parallel recursion at k=5 lifted Gemini under OpenCode to 80.8 on WorkBuddy
- The framework extends the 7-file split from earlier Agentic Harness Engineering work by 1 component
- Requires Linux, Python 3.12+, uv, and Vertex AI for model-backed runs
The T5 e-Paper Basic is a $34.04 development board featuring an ESP32-S3 microcontroller, 16MB flash, 8MB PSRAM, and a 4.7-inch ultra-low-power e-paper display with 540x960 resolution and 16 gray levels.
- Driver IC for the display is ED047TC1
- Includes I2C AXP2602 battery gauge, XL9555 I/O expander, and TF card slot
- Ships from local warehouses in Germany, USA, and Canada
- Driver IC for the display is ED047TC1
- Includes I2C AXP2602 battery gauge, XL9555 I/O expander, and TF card slot
- Ships from local warehouses in Germany, USA, and Canada
Sofia Lindström writes that cloud GPU pricing has diverged sharply in 2026, with AWS offering the lowest single-H100 on-demand rate at $6.88/hr while Azure's eight-GPU node pushes per-GPU costs to $12.29/hr. Azure leads in spot discounts at roughly 80% and object storage at $0.0184/GB-month, but its GPU premium makes it the priciest option for accelerator-heavy workloads.
- Google Cloud enforces an eight-GPU on-demand minimum for H100s, forcing small teams to pay for six unused accelerators
- Specialist GPU clouds like RunPod price H100s at $2.89/hr, a 2.4x discount versus AWS's on-demand rate
- Secondary charges (storage, public IP, egress) add 25-40% on top of advertised VM rates across all three hyperscalers
- AWS P5 GPU quota approval requires written justification and takes 3-7 business days
- Google Cloud enforces an eight-GPU on-demand minimum for H100s, forcing small teams to pay for six unused accelerators
- Specialist GPU clouds like RunPod price H100s at $2.89/hr, a 2.4x discount versus AWS's on-demand rate
- Secondary charges (storage, public IP, egress) add 25-40% on top of advertised VM rates across all three hyperscalers
- AWS P5 GPU quota approval requires written justification and takes 3-7 business days
Alex Davis writes that natural language processing offers product data scientists a complementary tool to strengthen traditional behavioral and transactional models. By extracting sentiment, topics, and semantic embeddings from support tickets, reviews, and survey responses, teams can improve accuracy across churn prediction, lifetime value, segmentation, and demand forecasting. The article emphasizes that text shifts often occur before numerical usage data does, allowing NLP-derived features to serve as early signals in prediction pipelines.
- The workflow involves aggregating NLP outputs into features that join directly onto existing behavioral tables for model training
- Embeddings help solve the cold-start problem for new customers and items in recommendation and CLV systems
- Simple A/B comparison tests between behavioral-only and behavioral-plus-text models validate whether text features add predictive value
- The workflow involves aggregating NLP outputs into features that join directly onto existing behavioral tables for model training
- Embeddings help solve the cold-start problem for new customers and items in recommendation and CLV systems
- Simple A/B comparison tests between behavioral-only and behavioral-plus-text models validate whether text features add predictive value
The c-logo-s initiative writes a chronologically organized history of the programming language Logo and the broader theoretical and institutional currents that shaped it, tracing origins from 1930s formal logic and 1940s cybernetics through the language's 1960s inception at BBN to its influence on modern graphical and agent-based environments. The account frames Logo not as an isolated educational tool but as a convergence point for ideas from Turing, Church, Piaget, the Macy Conferences, and early artificial intelligence research, detailing how collaborative efforts by Wallace Feurzeig, Cynthia Solomon, Daniel Bobrow, and Seymour Papert transformed interactive computing into a medium for exploring mathematics and cognitive development. The text further outlines Logo's commercial and academic distribution across the microcomputer era, its integration with physical robotics through LEGO and the MIT Media Lab, and its conceptual legacy in contemporary systems like StarLogo, NetLogo, Scratch, and Snap!.
Tonotopia is an upcoming mobile app that functions as a camera for sound, visually representing live audio as a spiral of notes where pitch is mapped by radial distance from the center. It features a free tier with live visualization and a premium option that adds tools like extended clips and a session card, targeting a wide range of users including singers, educators, creators, and audio professionals.
- Low frequencies appear near the center while high frequencies sit near the rim
- Visualizes harmonics as faint dots stacked above the fundamental note
- Microphone audio processing stays on the device
- Includes a practice mode with a chromatic tuner and solfège game
- Merchandise is available now with a launch planned for November 2026
- Low frequencies appear near the center while high frequencies sit near the rim
- Visualizes harmonics as faint dots stacked above the fundamental note
- Microphone audio processing stays on the device
- Includes a practice mode with a chromatic tuner and solfège game
- Merchandise is available now with a launch planned for November 2026
Matt Waxman writes that AI coding tools and agentic workflows have eliminated the translation layer between domain expertise and software production, enabling non-engineers to build working applications without traditional syntax knowledge. The article argues this shift redefines the engineer's role from craftsperson to infrastructure provider, increasing the value of those who design robust platforms, set standards, and maintain system architecture rather than just writing code manually.
- The article is a sponsored post by Precisely, with Insight Partners holding investments in both the sponsor and The New Stack.
- Two primary challenges identified are quality assurance of AI-generated code and the expansion of technical debt due to unowned services built by non-traditional users.
- The author posits that the demand for deep technical expertise is actually increasing as engineers become multipliers who support a larger population of builders.
- The article is a sponsored post by Precisely, with Insight Partners holding investments in both the sponsor and The New Stack.
- Two primary challenges identified are quality assurance of AI-generated code and the expansion of technical debt due to unowned services built by non-traditional users.
- The author posits that the demand for deep technical expertise is actually increasing as engineers become multipliers who support a larger population of builders.
Anthropic's Frontier Red Team writes that Claude solved a decades-old cryptographic mystery by identifying how the NIST elliptic curve seed parameters were generated. The seeds were not the result of a hash algorithm but were produced by the C library's `lrand48` pseudorandom number generator initialized with a Unix timestamp. This discovery resolves long-standing suspicion that the curves might contain intentional backdoors, as the extremely weak and transparent generation method leaves little room for subversion. The team also identified the origin of several base points for older curves, which were generated using Arjen Lenstra's FreeLIP package with specific small integer seeds.
- The prime-curve seeds were generated between May 1997 and April 1998.
- Four binary-curve seeds were generated on April 17, 1998.
- The base points for P-192 and five ANSI X9.62 curves were traced to the FreeLIP RNG.
- The B-163 seed and base points for P-224, P-256, P-384, and P-521 remain unexplained.
- The prime-curve seeds were generated between May 1997 and April 1998.
- Four binary-curve seeds were generated on April 17, 1998.
- The base points for P-192 and five ANSI X9.62 curves were traced to the FreeLIP RNG.
- The B-163 seed and base points for P-224, P-256, P-384, and P-521 remain unexplained.
>The cause of polysemanticity in early model organisms wasn’t superposition, but several ordinary, mostly fixable issues. We can now understand most neurons in models up to trillions of parameters, and think the outlook for mechanistic interpretability may be far better than we thought.
